A contact center manages all client contacts of a business or other entity through a variety of mediums, such as telephone, fax, letter, video, webforms, e-mail and, increasingly, online live chat. Distinct from call centers, that purely handle telephone correspondence, contact centers have a variety of roles that combine to provide an all encompassing solution to client and customer contact.
Contact centers have many different configurations.
A common type of contact center employs queues of contact center agents and work items and complex work assignment algorithms to provide optimal customer service. For example, in skill-based queues a work item queue is paired with a corresponding resource queue. When work items are received at the Automated Contact Distributor (ACD), the attributes of the work item are analyzed, and the work item is placed in a specific queue based on its attributes. Similarly, when a contact center resource (often an agent) comes on line they are assigned to one or more resource queues that also have a corresponding skillset associated therewith. Since skill queues are provided in work item/resource pairs, the next available agent in a resource queue is assigned the next work item waiting in the work item queue.
To improve efficiency, a contact center will typically segment contacts into many different queues. This segmentation may be by service, language, media type, region, and/or customer type. This can quickly result in many thousands of queues. Each of these queues needs to be configured, managed, monitored and reported on. Also, as agents gain new skills and improve their expertise levels, there is a need to constantly reassign agents to queues. Furthermore, when an agent gains new skills there is a significant cost in administration and operational costs of the contact center. Complexity increases because agents are typically in multiple queues simultaneously, and the new skills of an agent need to be updated in all relevant queues. Updating these changes in agent skills is a time-consuming and expensive task, which usually has to be performed with some amount of manual oversight. All of these factors add significant complexity and cost to the running of the center.
To address these issues, a queueless contact center has been developed. A queueless contact center discards queues and uses pools of resources, work items and qualifier sets and creates a qualification bit map for each pool. One-to-one optimal matching of work items and resources can be achieved by determining which resources are qualified to be assigned to a selected work item, which qualified resources are eligible to be assigned to the selected work item, and which eligible resources are most suitable to be assigned to the selected work item. The bit maps can enable ultra-fast mapping to determine which of the various resources is most suitable to be assigned to the selected work item.
The various contact center configurations face common challenges. Many potential customers in a wait queue to interact with a live agent often disconnect when they regard the time spent “on hold” as unproductive. To maintain customer interest while in queue, many contact centers play pre-recorded music and/or pre-recorded voice announcements that describe the company's products or services and/or tell the customer that his or her call is important. When customers are finally connected to an agent, the agent may spend valuable time gaining an understanding of the customer's skill and knowledge levels to respond appropriately to a customer's needs. Contact centers attempt to address this problem by acquiring information about the customer's needs via a traditional Interactive Voice Response (“IVR”) script. Other customer information may be inferred by mapping data obtained automatically by the contact center (such as caller ID or browser cookies) to records in a database.
These techniques can be annoying to customers rather than entertaining and rewarding. Particularly with customers having no prior transaction history with a contact center or enterprise, the current methods can do a poor job identifying the customer's skill and knowledge levels prior to establishing contact with an agent.